Exploring conformational and chemical spaces with advanced molecular simulation algorithms

The Dickson Lab is driven to develop the next generation of computational biophysics approaches and apply them to both learn general properties of protein-ligand interactions and to advance specific drug discovery projects. Their work is focused in two main areas. The first is using path sampling methods to explore biomolecular motions and processes that are relevant to ligand binding, such as the ligand-induced stabilization of molecular complexes and ligand- induced conformational change. A unique set of tools developed by the Dickson Lab includes weighted ensemble algorithms that can characterize ligand binding and unbinding events using continuous trajectories implemented without biasing forces. This is significant as these events typically occur on timescales that are up to billions of times longer than the duration of a molecular dynamics trajectory. Weights associated with these trajectories can be used to directly calculate observables such as ligand residence times, which can be crucial quantities for drug design. The second focus area is using machine learning methods to generate chemical structures and predict chemical properties. Their unique contributions in this area are at the intersection of data-driven and physics-driven approaches: their Flexible Topology algorithm is unique in that it allows for incorporation of generative chemical models into physics- based molecular simulations. This enables both pharmacophore-based virtual screening and generative algorithms that assemble “hits” inside binding pockets of interest, while allowing for the environment to respond to the presence of the ligand. Specific applications of this technology include de-orphanization of receptors and developing predictors of hERG-associated cardiotoxicity. Combined, the research in these areas provides a number of opportunities for exploring conformational space and chemical space, both separately and in tandem. The result will be a deep understanding of molecular motions that are relevant to ligand binding and new screening tools for the prediction of compounds that can achieve specific pharmacological goals. Project Number: 1R35GM161406-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Alexander Dickson | Institution: MICHIGAN STATE UNIVERSITY, EAST LANSING, MI | Award Amount: $593,975 | Activity Code: R35 | Study Section: Maximizing Investigators' Research Award - E Study Section[MRAE] View on NIH RePORTER: https://reporter.nih.gov/project-details/11259957